Qwen
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Qwen2.5-14B-Instruct

Text Generation

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JustinLin610 commited on 2 days ago

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@@ -13,7 +13,6 @@ license: apache-2.0
 Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
-- Pretrained on our **latest large-scale dataset**, encompassing up to **18T tokens**.
 - Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
 - Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
 - **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
@@ -83,7 +82,7 @@ For deployment, we recommend using vLLM. Please refer to our [Github](https://gi
 **Note**: Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**. We advise adding the `rope_scaling` configuration only when processing long contexts is required.
-## Evaultion & Performance
 Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/).

 Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
 - Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
 - Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
 - **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
 **Note**: Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**. We advise adding the `rope_scaling` configuration only when processing long contexts is required.
+## Evaluation & Performance
 Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/).